Experiment Settings. First, based on Python package skmultiflow [1]} (version 0.5.3), we generate a total of 8000 data streams, and each data stream has 2000 instances to simulate the prequential prediction errors of a learning model. Furthermore, each data stream only has one type of drift. Then, we select 1250 data streams from each type of concept drift, i.e., a total of 5000 data streams, as an original training dataset to extract meta-features. The remaining data streams are mixed as testing datasets. GaussianNB is imported from sklearn [2]} as the default learning model because it has a fast-processing speed and a partial fit option. As for the structures of the prototypical neural network, two embedding functions: a 4-layered fully connected neural network and recurrent neural network are selected as the nonlinear mapping. In the training phase, for each concept drift category, 5 support data streams are chosen randomly to act as the prototype and 15 query data streams are selected to evaluate the performance of the model. During the testing phase, 20 support data streams are randomly selected from the training dataset to perform as the final prototype. For all the experiments, ReLU activations are applied and the distance function is the cosine similarity function. All our models are trained via SGD with Adam, with a learning rate of \(10^{-3}\) .
<TABLE>